Prediction of Customer Attrition of Commercial Banks based on SVM Model

He, Benlan; Shi, Yong; Wan, Qian; Zhao, Xibin

doi:10.1016/j.procs.2014.05.286

Cited by 97 publications

(55 citation statements)

References 6 publications

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“…The experimental results showed that boosting algorithm provides a good separation of churn data when compared with a single logistic regression model. Benlan He [8] suggested a customer churn prediction methodology based on SVM model, and used random sampling method to improve SVM model by considering the imbalance characteristics of customer data sets. A support vector machine constructs a hyper-plane in a high-or infinitedimensional space, which can be used for classification.…”

Section: Mmentioning

confidence: 99%

A Survey on Customer Churn Prediction using Machine Learning Techniques

Kumar¹,

Chandrakala²

2016

IJCA

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The fast expansion of the market in every sector is leading to superior subscriber base for service providers. Added competitors, novel and innovative business models and enhanced services are increasing the cost of customer acquisition. In such a fast set up, service providers have realized the importance of retaining the on-hand customers. It is therefore essential for the service providers to prevent churn-a phenomenon which states that customer wishes to quit the service of the company. This paper reviews the most popular machine learning algorithms used by researchers for churn predicting, not only in banking sector but also other sectors which highly depends on customer participation.

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Section: Mmentioning

confidence: 99%

A Survey on Customer Churn Prediction using Machine Learning Techniques

Kumar¹,

Chandrakala²

2016

IJCA

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“…In the case of nonlinear classification [12], introduce the nonlinear mapping ¢ to project low-dimensional sample into a higher dimensional feature space and use kernel function k(Xi,X j ) to transform nonlinear classification into linear classification in this space. Then the optimal classification face function is:…”

Section: E Svm Algorithmmentioning

confidence: 99%

Exploring the risk dietary factors for the colorectal cancer

Tian

Zheng

Zhang

et al. 2015

2015 IEEE International Conference on Progress in Informatics and Computing (PIC)

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The research target is to explore the key biomarkers of colorectal cancer by studying the impact of dietary factors on colorectal cancer. We first employed statistical methods to preprocess experimental data. Then, relief algorithm is employed to extract key features in the dietary data set. Finally, supporting vector machine (SVM) is used to classify the data set and compute classification accuracy. The results demonstrated that vegetables, seafood, eggs and milk have great impacts on the colorectal cancer. Therefore, we concluded that integrating relief algorithm with SVM model can explore the key biomarkers for colorectal cancer, while the investigation of the interactions among these features needs further research.

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“…This prominent characteristic of the SVM could perfectly solve the critical problem that the actual analysis results were not as accurate as expected using the traditional approaches when the number of samples for establishing model was smaller. A more detailed description of SVM can be found in [11][12].…”

Section: Identification Of Oils Based On Svm and Gamentioning

confidence: 99%

NIR Spectroscopy and NMF Algorithm for Identification of Oil Pollutants in Water

Tan¹,

Zhao²,

Guo³

2014

Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-Oil pollutants is one of the major pollution sources in water. Accurate, rapid, and convenient detection method of oil pollutants in water has very important theoretical value and practical significance. The combination of near-infrared spectroscopy (NIR) and chemometrics is ideal for such a situation. NIR spectroscopy is a powerful and effective technique. traditional NIR methods do not take full account of the absorbance data non-negative characteristics, resulting in the analysis lack of reasonable explanation. In this paper, the qualitative discriminate method of single species oil contaminants based on nonnegative matrix factorization feature extraction combined with support vector machine classification algorithm is studied. Non-negative matrix factorization algorithm and support vector machine classifier parameters on classification accuracy are discussed in depth to optimize NIR qualitative classification model. The present method has a good identification effect and strong generalization ability ， and can work as a new method for rapid identification of oil pollutants in water．

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Prediction of Customer Attrition of Commercial Banks based on SVM Model

Cited by 97 publications

References 6 publications

A Survey on Customer Churn Prediction using Machine Learning Techniques

A Survey on Customer Churn Prediction using Machine Learning Techniques

Exploring the risk dietary factors for the colorectal cancer

NIR Spectroscopy and NMF Algorithm for Identification of Oil Pollutants in Water

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